in this paper, non parametric data envelopment analysis (dea) was subjected to the energy data of wheat producers in punjab state, india, and technical, pure technical and scale efficiencies were calculated for farms both category wise and zone wise. the main objective was to determine the strength of the correlation between non-parametric efficiencies and indices such as energy ratio and speci...

sueyoshi and sekitani in 2007 presented a paper that explores the measurement of returns to scale under a possible occurrence of multiple solution in a reference set and a supporting hyperplane. the occurrence ofmultiple solutions is classified into type i and type ii. type i is an occurrence of multiple solutions in a reference set. type ii is an occurrence of multiple solutions on a supportin...

data envelopment analysis (dea) is a branch of management, concerned with evaluating the performances of homogeneous decision making units (dmus). the performances of dmus are affected by the amount of sources that dmus used. usually increases in inputs cause increases in outputs. however, there are situations where increase in one or more inputs generate a reduction in one or more outputs. in ...

data envelopment analysis (dea) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (dmus) where multiple inputs and outputs exist. in the conventional applications of dea, the data are considered as specific numerical values with explicit designation of being an input or output. however, the observed values of the data are sometimes imprec...

in this paper, presenting two simple methods for ranking of efficient dmus in dea models that included to add one virtual dmu as ideal dmu and is using the additive model. note that, we use an ideal point just for comparing efficient dmus with. although these methods are simple, they have ability for ranking all efficient dmus, extreme points and the others, also they are capable of ranking the...

for better guiding a system, senior managers should have accurate information. using data envelopment analysis (dea) help managers in this objective. thus, many investigations have been made in order to find the most productive scale size (mpss) for the evaluating decision making units (dmus). in this paper we consider this case where there exist subsets of input and output variables to be inte...

in some situations the producers desire to maximize total profit of decision making units (dmus) while the inputs and outputs prices of dmus change from one time period to another. in this paper, the researchers develop productivity index when producers are going to maximize total profit when the price of inputs and outputs are known.the proposed method uses all price information about inputs a...

background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. we have presented two ordinal–variables clustering exampl...

in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

this paper proposes an alternative approach for efficiency analysis when a set of dmus uses interval scale variables in the productive process. to test the influence of these variables, we present a general approach of deriving dea models to deal with the variables. we investigate a number of performance measures with unrestricted-in-sign interval and/or interval scale variables.